Abstract
Object pose estimation of transparent objects remains a challenging task in the field of robot vision due to the immense influence of lighting, background, and reflections. However, the edges of clear objects have the highest contrast, which leads to stable and prominent features. We propose a novel approach by incorporating edge detection in a pre-processing step for the tasks of object detection and object pose estimation. We conducted experiments to investigate the effect of edge detectors on transparent objects. We examine the performance of the state-of-the-art 6D object pose estimation pipeline GDR-Net and the object detector YOLOX when applying different edge detectors as pre-processing steps (i.e., Canny edge detection with and without color information, and holistically-nested edges (HED)). We evaluate the physically-based rendered dataset Trans6D-32 K of transparent objects with parameters proposed by the BOP Challenge. Our results indicate that applying edge detection as a pre-processing enhances performance for certain objects.
Abstract (translated)
透明物体的姿态估计在机器人视觉领域中仍然是一个具有挑战性的任务，这主要是由于光照、背景和反射等因素的显著影响。然而，清晰物体的边缘部分对比度最高，从而提供了稳定且突出的特征。为此，我们提出了一种新的方法，在对象检测和姿态估计任务之前引入边缘检测作为预处理步骤。 我们在实验中研究了不同的边缘检测器对透明物体的影响，并评估了几种先进的算法：6D物体姿态估计流水线GDR-Net及目标检测器YOLOX在应用不同边缘检测器（如Canny边缘检测是否结合颜色信息，以及全卷积网络生成的holistically-nested edges (HED)）作为预处理步骤时的表现。我们使用了BOP Challenge提出的物理基础渲染数据集Trans6D-32K来评估透明物体。 实验结果表明，在特定的对象上应用边缘检测作为预处理可以增强性能。
URL
https://arxiv.org/abs/2502.12027
